Understand it,
don't memorize it.
A community-built path through hard subjects, taught the way 3Blue1Brown teaches: visually, from the ground up, without skipping a single brick. Every step earns the next, and tells you exactly what to understand before it.
The Path to Machine Learning
Start with arrows on a grid. End understanding how a machine learns. Every step earns the next; no brick is skipped.
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1
Linear Algebra 22 concepts · start here
Data is vectors and models are transformations. This is the ground everything else stands on.
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2
Calculus planned · open for a PR
The math of change. A model learns by asking how a tiny nudge to its knobs changes its error.
what a function is · the limit · the derivative · derivative as local zoom · the chain rule · partial derivatives · the gradient · integrals as accumulation
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3
Probability planned · open for a PR
The math of uncertainty. Real data is noisy, and models reason in odds, not certainties.
sample space and events · probability as measure · conditional probability · Bayes' theorem · random variables · distributions · expectation and variance · the normal distribution
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4
Optimization planned · open for a PR
Learning is finding the lowest point of an error surface. This is how you get there.
what optimization is · critical points · convexity · gradient descent · the learning rate · stochastic gradient descent · local vs global minima
needs first: Calculus, Linear Algebra
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5
Statistics planned · open for a PR
How to trust what data says, and how to catch a model that has fooled itself.
population vs sample · estimation · bias and variance · sampling distributions · confidence intervals · hypothesis testing · overfitting
needs first: Probability
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6
Machine Learning planned · open for a PR
The destination. Every domain before it was already walking here.
what learning means · linear regression · loss functions · training as optimization · classification · regularization · neural networks · backpropagation · PCA (eigenvectors, revisited)
needs first: Linear Algebra, Calculus, Probability, Optimization, Statistics
No missing bricks
No concept assumes something you were never taught. Every prerequisite is a link, and it exists, checked automatically on every contribution. A roadmap with a missing brick is not a roadmap; it is a trap. The greyed steps above are the plan, waiting for someone to build them. Claim one.